MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES.
Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors.
Duke Scholars
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Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics
Citation
Published In
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics